Résumé
Biodiversity is a vital component of natural capital that significantly influences ecosystem functions and provides essential services and benefits, ranging from food security to cultural heritage. However, species are currently disappearing at a rate 100 to 1,000 times higher than the natural extinction rate. Coastal ecosystems are particularly concerning: they are among the most vulnerable due to their exposure to cumulative anthropogenic pressures while biodiversity knowledge is lacking. Supported by the French National Space Agency (CNES) and endorsed by the Space Climate Observatory (SCO), the BioEOS project aims to develop observation tools to characterize the spatiotemporal dynamics of coastal biodiversity. This initiative will map changes and produce operational indicators to assist in conservation and restoration efforts in the Marine Protected Area (MPA). The project primarily takes advantage of image time series from multispectral (Pleiades, Sentinel-2, Venus) and hyperspectral (EnMAP, PRISMA) satellite systems. A set of selected biodiversity proxy metrics are extracted using high SRL (Scientific Readiness Level) algorithms that have been widely used by the benthic scientific community. These algorithms encompass the inversion of radiative transfer models, machine learning-based scene segmentation, spectral unmixing, pansharpening, and the calculation of spectral indices. This approach enables to generate valuable information on bathymetry, bottom/habitat type abundances and distributions, as well as water column properties estimations. Coral reef and seagrasses of Southwestern Indian Ocean region (La Réunion, Mayotte, Glorieuses and Bassas da India) are the first targeted ecosystems for this experimentation. We present the main advancements of a demonstrator providing key essential variables contributing to various end uses through four distinct use cases. Additionally, we will discuss the strengths and limitations of the satellite systems employed, in light of the initial objectives set forth.